What is a k-median?
A clustering algorithm closely related to k-means.
k-median explained in plain English
A clustering algorithm closely related to k-means. The practical difference between the two is as follows: - In k-means, centroids are determined by minimizing the sum of the squares of the distance between a centroid candidate and each of its examples. - In k-median, centroids are determined by minimizing the sum of the distance between a centroid candidate and each of its examples. Note that the definitions of distance are also different: - k-means relies on the Euclidean distance from the centroid to an example. (In two dimensions, the Euclidean distance means using the Pythagorean theorem to calculate the hypotenuse.) For example, the k-means distance between (2,2) and (5,-2) would be:
- k-median relies on the Manhattan distance from the centroid to an example. This distance is the sum of the absolute deltas in each dimension. For example, the k-median distance between (2,2) and (5,-2) would be:
Example
Practitioners refer to k-median when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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